Key result
An ECG signal de-noising method using wavelet energy and a sub-band smoothing filter effectively removed noise from noisy ECG signals compared to existing methods.
Why the study?
ECG signals are essential for diagnosing and analysing cardiac disease but are susceptible to noise contamination that impairs their application value.
Does an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter improve signal quality compared to existing methods?
Does an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter improve signal quality compared to existing methods?
A novel ECG de-noising method using wavelet energy and a sub-band smoothing filter effectively removes noise and improves signal quality compared to traditional methods.
May enhance noisy ECG interpretation; extends prior wavelet methods but leaves clinical validation open.
Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.
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Zhang et al. (2019) studied Cardiac disease (ECG signals). ECG signal de-noising method using wavelet energy and a sub-band smoothing filter vs. Existing methods (traditional wavelet threshold de-noising) was evaluated on Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). An ECG signal de-noising method using wavelet energy and a sub-band smoothing filter effectively removed noise from noisy ECG signals compared to existing methods.
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